code: def normalize(X):
      """equivalent to scipy.preprocessing.normalize on sparse matrices
      , but lets avoid another dependency just for a small utility function
      """
      X = coo_matrix(X)
      X.data = X.data / sqrt(bincount(X.row, X.data**2))[X.row]
      return X
  
imports: import numpy
  from numpy import bincount, log, log1p, sqrt
  from scipy.sparse import coo_matrix, csr_matrix
  from ._nearest_neighbours import NearestNeighboursScorer, all_pairs_knn
  from .recommender_base import RecommenderBase
  from .utils import _batch_call
label_desc: Use image node_id 2 for calling a global function with example usage: normalize(X) and returns: X